Editing IPCC:AR6/WGII/Cross-Chapter-Paper-5 (section)

== CCP5.3 Projected Impacts and Risks in Mountains ==

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=== CCP5.3.1 Synthesis of Projected Impacts ===

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Declines and extinctions have been projected in a range of montane plants and animal species, including rare endemic species and sub-species due to climate change ( ''medium evidence, high agreement'' ) ( [[#Li--2017|Li et al., 2017]] ; [[#Ashrafzadeh--2019|Ashrafzadeh et al., 2019]] ; [[#Brunetti--2019|Brunetti et al., 2019]] ; [[#Zhang--2019b|Zhang et al., 2019b]] ; [[#Manes--2021|Manes et al., 2021]] ). Up to 84% of endemic mountain species are found to be at risk of extinction ( [[#Manes--2021|Manes et al., 2021]] ). Using a simple model, [[#Helmer--2019|Helmer et al. (2019)]] predict a large-scale contraction in the next 25 years of alpine ecosystems above tropical mountain cloud forest in the Andes due to tree invasion. Topographic complexity can smoothen and delay the transition of montane forests in terms of size and composition for warming up to 3°C GWL ( [[#Albrich--2020|Albrich et al., 2020]] ).

Hydrological changes will determine how some ecosystems change, more so than changes in temperature. For example, Dwire et al. (2018) found that changes in riparian areas, wetlands and forests were likely a result of climate change in the Blue Mountains in Oregon, USA, as a result of altered snowpack, hydrologic regimes, drought and wildfire. In the Bolivian Cordillera Real, wetland cover variations were associated with increases in extreme precipitation events and glacier melting over the 1984–2011 period but might be reversed with predicted future decreases in both total precipitation and glacier run-off ( [[#Dangles--2017|Dangles et al., 2017]] ). About 30% of the wetland area in the Great Xing’an Mountains in northeastern China has been projected to disappear by 2050, with this value doubling by 2100 under the CGCM3-B1 scenario ( [[#Liu--2011|Liu et al., 2011]] ).

Climate change impacts on food, fibre and ecosystem products will be highly variable across mountain regions ( ''medium confidence'' ) ( [[#Briner--2013|Briner et al., 2013]] ; [[#Rasul--2015|Rasul and Hussain, 2015]] ; [[#Mina--2017|Mina et al., 2017]] ; [[#Palomo--2017|Palomo, 2017]] ; [[#Said--2019|Said et al., 2019]] ; [[#Xenarios--2019|Xenarios et al., 2019]] ) (Sections 10.4, 12.3, 13.5 and 14.4). In some regions, tree crops that are cultivated at certain elevations may reach the limit of their agroclimatic plasticity, for instance for crop types that require winter chills and where projected growing conditions are too warm ( [[#Buerkert--2020|Buerkert et al., 2020]] ). In the European Alps, agricultural production in some areas may benefit from temperature rises, as total productivity in grasslands is projected to increase ( [[#Mitter--2015|Mitter et al., 2015]] ; [[#Grüneis--2018|Grüneis et al., 2018]] ), whereas some areas in Asia and South America heavily dependent on glacier- and snow-fed irrigation will be at risk of food insecurity ( [[#Rasul--2019|Rasul and Molden, 2019]] ). In a study in Eastern Pamir, [[#Mętrak--2017|Mętrak et al. (2017)]] found that summer droughts and water changes lead to functional transformations of the wetland ecosystems which can affect food security of the local population. Climate change affects the phenology of plants ( [[#Harish--2012|Harish et al., 2012]] ; [[#Gaira--2014|Gaira et al., 2014]] ; [[#Maikhuri--2018|Maikhuri et al., 2018]] ), secondary metabolites ( [[#Chang--2016|Chang et al., 2016]] ; [[#Kumar--2020|Kumar et al., 2020]] ) and pharmacological properties of medicinal plants ( [[#Gairola--2010|Gairola et al., 2010]] ; [[#Das--2016|Das et al., 2016]] ).

Water resources in mountains and dependent lowlands will continue to be strongly impacted by climate change throughout the 21st century ( ''high confidence'' ). The difference in impacts will be particularly strong in regions that greatly depend on glacier and snowmelt and, in pronounced dry seasons ( ''high confidence'' ), in regions including Central Asia, South Asia, tropical and subtropical western South America and southwestern North America ( [[#Huss--2018|Huss and Hock, 2018]] ; [[#Hock--2019|Hock et al., 2019]] ; [[#Immerzeel--2020|Immerzeel et al., 2020]] ). Glaciers are expected to continue to lose mass throughout the 21st century, with higher mass loss under high emission scenarios (AR6 WGI [[IPCC:Wg2:Chapter:Chapter-9|Chapter 9]] ( [[#Fox-Kemper--2021|Fox-Kemper et al., 2021]] )). Many low-elevation and small glaciers around the world will lose most of their total mass at 1.5°C GWL ( ''high confidence'' ) ( [[#Marzeion--2018|Marzeion et al., 2018]] ; [[#Vuille--2018|Vuille et al., 2018]] ; [[#Hock--2019|Hock et al., 2019]] ; [[#Zekollari--2020|Zekollari et al., 2020]] ; [[#Fox-Kemper--2021|Fox-Kemper et al., 2021]] ) (WGI 9.5). For tropical and mid-latitude mountains, around half of the current ice mass can be preserved under low-emission scenarios, while between two-thirds and up to more than 90% will be lost under high emission scenarios compared to the 2000s ( ''medium confidence'' ) ( [[#Schauwecker--2017|Schauwecker et al., 2017]] ; [[#Vuille--2018|Vuille et al., 2018]] ; [[#Hock--2019|Hock et al., 2019]] ; [[#Fox-Kemper--2021|Fox-Kemper et al., 2021]] ) (WGI 9.5). Significant differences in impacts between the emission scenarios have also been assessed for declines in snow depth or mass at lower elevations [10 to 40% for RCP2.6 and 50 to 90% for RCP 8.5 by the end of the century ( [[#Hock--2019|Hock et al., 2019]] )]. However, limitations in long-term climate, glaciological and hydrological monitoring data add uncertainty to the current understanding and adaptation support, for example, when peak water is reached in different mountain catchments ( [[#Salzmann--2014|Salzmann et al., 2014]] ; [[#Hock--2019|Hock et al., 2019]] ). Furthermore, context-specific sociocultural and economic factors can magnify or moderate impacts related to hydrological change ( [[#McDowell--2021a|McDowell et al., 2021a]] ).

The dependence of lowland populations on mountain water resources will grow by mid-century across several climate and socioeconomic scenarios, and several seasonally dry or semiarid mountain regions (e.g., parts of South Asia, North America) are projected to be highly dependent on such resources ( ''medium confidence'' ) ( [[#Viviroli--2020|Viviroli et al., 2020]] ) (Figure CCP5.2). Changing sediment, nutrient and pollutant flows due to climatic and non-climatic drivers will impact populations and economic sectors ( ''medium evidence, high agreement'' ). Hydropower in all mountain regions will experience higher fluxes of water and sediment in some seasons but lower water flow with demand from other water uses (e.g., irrigation) ( [[#Chevallier--2011|Chevallier et al., 2011]] ) in other seasons ( [[#Beniston--2014|Beniston and Stoffel, 2014]] ; [[#Gaudard--2014|Gaudard et al., 2014]] ; [[#Majone--2016|Majone et al., 2016]] ; [[#Caruso--2017a|Caruso et al., 2017a]] , b; [[#Patro--2018|Patro et al., 2018]] ). Recharge from groundwater and its buffer function is expected to decrease in the longer term ( [[#Somers--2020|Somers and McKenzie, 2020]] ). Glacier and snow depth or mass decline will impact current hydropower facilities and production in various complex ways, requiring changes in hydropower management, with further potential for evidence-informed solutions ( [[#Gaudard--2014|Gaudard et al., 2014]] ; [[#Schaefli--2015|Schaefli, 2015]] ; [[#Schaefli--2019|Schaefli et al., 2019]] ). On the other hand, deglaciation in mountain regions opens topographic space and, thus, potential for additional long-term hydropower development and production ( [[#Haeberli--2016a|Haeberli et al., 2016a]] ), with an estimated additional production of up to several hundred terawatt-hours per year, a potentially important contribution to national energy supplies, in particular in the High Mountain Asia region ( [[#Farinotti--2019|Farinotti et al., 2019]] ). However, water supply from glacier melt will decrease once source glaciers pass peak discharge ( [[#Huss--2018|Huss and Hock, 2018]] ), and the areas with available sediment will grow as glaciers shrink, posing potential risks to downstream populations and assets ( ''high confidence'' ) ( [[#Lane--2019|Lane et al., 2019]] ).

Since SROCC ( [[#Hock--2019|Hock et al., 2019]] ), several new studies have addressed projected impacts of future climate change on snow reliability in ski resorts, complementing previous findings or bridging existing knowledge gaps for winter tourism. This includes, in particular, new studies for China (An et al., 2019; [[#Fang--2019|Fang et al., 2019]] ), showing that average ski seasons are projected to shorten (−4 to −61% for RCP4.5; −6 to −79% RCP8.5 in the 2050s) along with increases in snow-making water demand (27 to 51% for RCP4.5; 46 to 80% for RCP8.5 in the 2050s), with large differences across the country. Changes in future snow reliability are projected across Europe at the national or pan-European scale ( [[#Demiroglu--2019|Demiroglu et al., 2019]] ; [[#Steiger--2020|Steiger and Scott, 2020]] ; [[#Morin--2021|Morin et al., 2021]] ), highlighting strong contrasts at the local (across ski resort size and/or elevation range, or local social or environmental context) and continental scales. Higher-latitude and high-elevation locations generally exhibit delayed declines in snow reliability compared to lower-latitude and lower-elevation locations ( ''high confidence'' ), consistent with assessment conclusions reached in SROCC ( [[#Hock--2019|Hock et al., 2019]] ). In general, climate change impacts and risks to ski tourism are found to be spatially heterogeneous, within and across local and international markets, with potential for significant disruptions to related socioeconomic sectors due to a growing mismatch between ski area supply and skier demand in the coming decades ( ''high confidence'' ) ( [[#Fang--2019|Fang et al., 2019]] ; [[#Hock--2019|Hock et al., 2019]] ; [[#Steiger--2021|Steiger et al., 2021]] ). These disruptions are plausible, even though a fraction of current ski resorts could technically operate in comparatively favourable locations (elevation, latitude) and operating models (business models, sociocultural assets and conditions, governance) ( [[#Steiger--2020|Steiger et al., 2020]] ).

Severe damage and disruptions to people and infrastructure from floods are projected to increase in Northwestern South America (NWS), South Asia (SAS), Tibetan Plateau (TIB) and Central Asia (WCA) between 1.5°C and 3°C GWL, mainly driven by river floods and an increase in the number of glacial lakes with high potential for outburst ( ''high confidence'' ) ( [[#Drenkhan--2019|Drenkhan et al., 2019]] ; [[#Motschmann--2020b|Motschmann et al., 2020b]] ; [[#Furian--2021|Furian et al., 2021]] ; [[#Zheng--2021|Zheng et al., 2021]] ). For example, the formation of new lakes at the foot of steep icy peaks largely extends the hazard zones with respect to the earlier situation without lakes ( [[#Haeberli--2016b|Haeberli et al., 2016b]] ). Projected changes in ice and snowmelt, as well as seasonal increases in extreme rainfall and permafrost thaw, will favour chain reactions and cascading processes, which can have devastating downstream effects well beyond the site of the original event ( ''high confidence'' ) ( [[#Cui--2015|Cui and Jia, 2015]] ; [[#Beniston--2018|Beniston et al., 2018]] ; [[#Terzi--2019|Terzi et al., 2019]] ; [[#Vaidya--2019|Vaidya et al., 2019]] ; [[#Shugar--2021|Shugar et al., 2021]] ). The incidence of disasters is projected to increase in the future because some hazards will become more pervasive, with an increase in the exposure of people and infrastructure with future environmental and socioeconomic changes either contributing to reduce or enhance these disaster risks ( ''medium confidence'' ) ( [[#Klein--2019b|Klein et al., 2019b]] ).

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=== CCP5.3.2 Key Risks Across Sectors and Regions ===

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Key risks are derived from the detection and attribution assessment (CCP5.2.7) and from the projected impacts and risks (CCP5.3.1). The assessment is informed by evidence in the regional and sectoral chapters and supports the key risk assessment in Chapter 16. Four key risks (KR1 to KR4) have been identified in this CCP and are presented in Sections CCP5.3.2.1 –CCP5.3.2.4 (see SMCCP5.4 for methodology and references).

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==== CCP5.3.2.1 KR1: People and Infrastructures at Risks from Landslides and Floods ====

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The amount of people and infrastructure at risk of landslides will increase in regions where the frequency and intensity of rainfall events is projected to rise ( [[#Gariano--2016|Gariano and Guzzetti, 2016]] ; [[#Haque--2019|Haque et al., 2019]] ). Extreme precipitation in major mountain regions is projected to increase, leading to consequences such as floods and landslides ( ''medium confidence'' ). Rain-on-snow events, which can accelerate all flood stages and result in widespread consequence for societies, are projected to increase between 2°C and 4°C GWL (but decrease afterwards) (SROCC [[IPCC:Wg2:Chapter:Chapter-2|Chapter 2]] ( [[#Hock--2019|Hock et al., 2019]] ), AR6 WGI [[IPCC:Wg2:Chapter:Chapter-12|Chapter 12]] ( [[#Ranasinghe--2021|Ranasinghe et al., 2021]] )). There is high confidence that glacial retreat, slope instabilities and heavy precipitation will affect landslides and flood activities, although for landslides there are considerable uncertainties in the direction of change ( [[#Patton--2019|Patton et al., 2019]] , AR6 WGI [[IPCC:Wg2:Chapter:Chapter-12|Chapter 12]] ( [[#Ranasinghe--2021|Ranasinghe et al., 2021]] )).

Future risk consequences considered to be severe include, for example, an increase of 10–20% compared to present of the population exposed to landslide activities in certain regions (e.g., High Mountain Asia) ( [[#Kirschbaum--2020|Kirschbaum et al., 2020]] ). This does not consider the expected increase in landslide activity relating to glacier and permafrost changes ( [[#Picarelli--2021|Picarelli et al., 2021]] , SROCC [[IPCC:Wg2:Chapter:Chapter-2|Chapter 2]] ( [[#Hock--2019|Hock et al., 2019]] )), so it is expected to be a conservative estimate. Other severe consequences are on average a projected twofold increase in the number of people exposed to inland flooding between 2°C and 4°C, with the highest increases in South Asia, Southeast Asia and South America ( ''high confidence'' in the direction of change and ''medium confidence'' in the absolute values because they are based on global studies) ( [[#Hirabayashi--2013|Hirabayashi et al., 2013]] ; [[#Allen--2016|Allen et al., 2016]] ; [[#Arnell--2016|Arnell and Gosling, 2016]] ; [[#Zheng--2021|Zheng et al., 2021]] ). Therefore, high to very high risks are expected between 2°C and 4°C GWL in several mountain regions (red and violet shaded bars in Figure CCP5.5). Many regions are projected to experience high risks due to the timing (potentially for severe consequences to happen sooner rather than later), the magnitude in terms of number of people and infrastructure affected and the persistence of hazard conditions (Figure CCP5.5, AR6 WGI [[IPCC:Wg2:Chapter:Chapter-12|Chapter 12]] ( [[#Ranasinghe--2021|Ranasinghe et al., 2021]] )). Comparatively, more severe risk consequences are expected under SSP3 and/or SSP4 given the high population projections in certain regions compared to SSP1 ( ''medium confidence'' ) ( [[#Kirschbaum--2020|Kirschbaum et al., 2020]] ) (Figure CCP5.1).

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[[File:0ee1b2d909960e374bb37e6125151519 IPCC_AR6_WGII_Figure_CCP5_005.png]]

'''Figure CCP5.5 |''' '''People and infrastructure in mountain regions at risk of landslides and/or floods for various GWLs.'''

'''Panel a)''' shows the level of risk assessed per AR6 WGI reference region (AR6 WGI Atlas ( [[#Gutiérrez--2021|Gutiérrez et al., 2021]] )). For some mountain regions, there is limited evidence for adequately assessing the level of risks against GWLs, so this is labelled ‘not assessed’.

'''Panel b)''' shows the level of risk aggregated at the continent scale and the principal hazards for which evidence was available and assessed. Methodological details and traceability are provided in SMCCP5.4, Figure SMCCP5.1, Table SMCCP5.16 and SMCCP5.18.

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==== CCP5.3.2.2 KR2: Risks to Livelihoods and the Economy from Changing Water Resources ====

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KR2 encompasses the relative and absolute dependence on water resources for economic activities and livelihood sustainment in mountain regions and in lowlands (Table SMCCP5.19). Particularly affected by changes in water resources will be regions with (seasonally) high dependence on snow and glacier melt, i.e., arid and semiarid zones in the Andes, Central Asia and the Upper Indus Basin ( [[#Huss--2017|Huss et al., 2017]] ; [[#Huss--2018|Huss and Hock, 2018]] ; [[#Viviroli--2020|Viviroli et al., 2020]] ) (Section CCP5.3.1).

Consequences that are considered severe refer to the magnitude (number of people and economic activities affected), timing (increase of water stress as early as mid-century in several regions) and likelihood (severe risk consequences are more ''likely'' where high population density is projected) ( ''high confidence'' ) (Figures CCP5.1 and CCP5.6, Section 4..5.7 and 4.7) ( [[#Fuhrer--2014|Fuhrer et al., 2014]] ; [[#Wijngaard--2018|Wijngaard et al., 2018]] ; [[#Biemans--2019|Biemans et al., 2019]] ; [[#Immerzeel--2020|Immerzeel et al., 2020]] ; [[#Viviroli--2020|Viviroli et al., 2020]] ). Severe consequences are that by mid-century more than half of agricultural regions equipped for irrigation are projected to be dependent on mountain runoff and could therefore be unsustainably using blue water (e.g., water from river, lakes and aquifers) ( [[#Viviroli--2020|Viviroli et al., 2020]] ) or that the number of people being water stressed will increase by 50% to 100% in areas already currently water stressed ( [[#Munia--2020|Munia et al., 2020]] ). Hotspot regions are those with large lowland populations depending on essential mountain water resource contributions and include river catchments such as the Ganges, Brahmaputra, Meghna, Yangtze, Nile, Niger, Indus, Euphrates-Tigris or Pearl ( ''high confidence'' ) ( [[#Viviroli--2020|Viviroli et al., 2020]] ) (Figure CCP5.6). Limited governance and integrated management of water resources, power and gender inequalities and level of disruption of local community practices also contribute to making risks more severe ( ''medium confidence'' ) ( [[#Lynch--2012|Lynch, 2012]] ; [[#Boelens--2014|Boelens, 2014]] ; [[#Wijngaard--2018|Wijngaard et al., 2018]] ; [[#Scott--2019|Scott et al., 2019]] ; [[#Immerzeel--2020|Immerzeel et al., 2020]] ). Consequences for hydropower are comparatively less severe than for agriculture and domestic/municipal use, although this depends on region and timing (see also [[IPCC:Wg2:Chapter:Chapter-5#5.2.2|Section 5.2.2.2]] ). For example, a study shows low risk to hydropower production in High Mountain Asia until the end of the century and even for warming levels beyond 3°C ( ''robust evidence, moderate agreement'' ) ( [[#Mishra--2020|Mishra et al., 2020]] ) ''.''

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'''Figure CCP5.6 |''' '''Risk levels assessed per AR6 WGI reference region (AR6 WGI Atlas (Gutiérrez et al.''' ''', 2021)).''' The majority of studies assessed focus on impacts up to mid-century (2030–2060) and for RCP-2.6, RCP-4.5 and RCP-6.0, which was converted into the corresponding warming level range 1.5°C–2.0°C GWL (Cross-Chapter Box CLIMATE in Chapter 1). Methodological details are provided in Section SMCCP5.4, Figure SMCCP5.1, Table SMCCP5.17 and SMCCP5.19. Due to the ''limited evidence'' available to determine risks against high GWLs and the relatively high uncertainty associated with future irrigation trends for the second half of the century (e.g., [[#Viviroli--2020|Viviroli et al., 2020]] ), assessment of risks associated with GWLs greater than 2.0°C GWL was not conducted.

Large-scale and transformative interventions can reduce the high-end impacts of changing water resources and in particular the risks of water scarcity (Section CCP5.4.1). These interventions have long lead times, are costly and may face institutional constraints ( [[IPCC:Wg2:Chapter:Chapter-4#4.7|Section 4.7]] ), resulting in adaptation shortfalls. Therefore, high-risk to very high-risk levels cannot be excluded in regions where other key risk characteristics, such as magnitude, timing and likelihood, are assessed as high due to potential losses (e.g., in many Asian regions) (Figure CC5.6, SMCCP5.4 and Table SMCCP5.16).

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==== CCP5.3.2.3 KR3: Risks of Ecosystem Change and Species Extinction ====

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Risks to mountain ecosystems and the services they provide to people are varied in magnitude, timing, likelihood and potential to adapt and place specific. However, many mountain ecosystems are already showing impacts of climate change (CCP5.3.1), reflecting the strong influence climate exerts in many situations and indicating that risks are significant and immediate and will ''likely'' increase in the near as well as long term. There is ''robust evidence'' ( ''high agreement'' ) of vegetation zones and individual species shifting to higher elevations (CCP5.2.1 and [[IPCC:Wg2:Chapter:Chapter-2#2.4|Section 2.4]] ), and projections indicate that current trends will continue and accelerate at higher rates of warming ( ''medium evidence, high agreement'' ) ( [[IPCC:Wg2:Chapter:Chapter-2#2.5|Section 2.5]] ).

Many mountain species are at risk of range contraction and ultimately extinction if dispersal at the upper range limit is slower than losses due to mortality at the lower range limit (observed for trees in the neotropics ( [[#Feeley--2013|Feeley et al., 2013]] ; [[#Duque--2015|Duque et al., 2015]] ) or if mountains are not high enough to allow species to move to higher elevations. [[#Ramirez-Villegas--2014|Ramirez-Villegas et al. (2014)]] modelled 11,012 species of birds and vascular plants in the Andes and found large decreases by 2050 (SRES-A2 scenario); in the absence of dispersal, 10% of species could become extinct. Even assuming unlimited dispersal, most of the Andean endemics would become severely threatened. Other modelling studies have also projected declines in a range of communities and species, including rare endemics ( [[#Zomer--2014a|Zomer et al., 2014a]] ; [[#Rashid--2015|Rashid et al., 2015]] ; [[#Bitencourt--2016|Bitencourt et al., 2016]] ; [[#Li--2017|Li et al., 2017]] ; [[#Rehnus--2018|Rehnus et al., 2018]] ; [[#Ashrafzadeh--2019|Ashrafzadeh et al., 2019]] ; [[#Zhang--2019b|Zhang et al., 2019b]] ; [[#Cuesta--2020|Cuesta et al., 2020]] ; [[#Hoffmann--2020|Hoffmann et al., 2020]] ).

Many treelines will continue to shift to higher elevations with increasing temperatures ( [[#Chhetri--2018|Chhetri and Cairns, 2018]] ), although very few are changing as fast as the climate ( [[#Liang--2016|Liang et al., 2016]] ; [[#Hansson--2021|Hansson et al., 2021]] ) and some are not moving or even shifting to lower elevations (CCP5.2.1). If treelines fail to shift uphill, this will pose a risk for species of the upper-montane forest that experience range contraction at their lower range limit but lack a suitable habitat to expand into beyond their upper range limit ( [[#Rehm--2015|Rehm and Feeley, 2015]] ). Changes in phenology can also pose risks to species and ecosystems (Chapter 2), including a potential desynchronisation of mutualistic relationships like pollination and increased freezing damage due to premature emergence from winter dormancy. In European broadleaf trees, for example, the upper elevational limits of different species involve a trade-off between maximising growing season length and limiting the risk of spring freeze damage ( [[#Vitasse--2012|Vitasse et al., 2012]] ; [[#Körner--2016|Körner and Spehn, 2016]] ).

A wide range of mechanisms can cause changes within ecological communities, some of which are hard to predict, but an increasing number of studies illustrate some of the risks which are expected to be most common. If treelines shift upwards, this will pose a risk for alpine species, which cannot compete with trees. This may lead to the extinction of alpine species on mountains where there is insufficient room for the alpine zone to shift uphill. Shifts in species distributions, and in particular shifts in ecosystem types, can cause changes in ecosystem function, which may in turn have cascading impacts on people, for example leading to increased exposure to diseases such as malaria at high elevation ( [[IPCC:Wg2:Chapter:Chapter-2#2.4.2.7.2|Section 2.4.2.7.2]] ) as vector distribution changes and wider impacts on ecosystem services ( [[IPCC:Wg2:Chapter:Chapter-2#2.5.3|Section 2.5.3]] ) such as water supply, flood alleviation and food.

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==== CCP5.3.2.4 KR4: Risk of Intangible Losses and the Loss of Cultural Values ====

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The risk of intangible losses and loss of cultural values is associated with the decline of ice and snow cover and temperature increase, as well as the increase in intangible harm from hazards such as floods and droughts ( ''high agreement, medium evidence'' ) ( [[#Diemberger--2015|Diemberger et al., 2015]] ; [[#Jurt--2015|Jurt et al., 2015]] ; [[#Vuille--2018|Vuille et al., 2018]] ; [[#Tschakert--2019|Tschakert et al., 2019]] ; [[#Vander%20Naald--2020|Vander Naald, 2020]] ). Losses are intangible because they characterise aspects which are difficult to quantify, i.e., loss of identity, loss of self-reliance, loss of rituals and traditions and place attachment ( [[#Allison--2015|Allison, 2015]] ; [[#Baul--2015|Baul and McDonald, 2015]] ; [[#Motschmann--2020a|Motschmann et al., 2020a]] ; [[#Schneiderbauer--2021|Schneiderbauer et al., 2021]] ). A global systematic analysis of case studies shows that this risk is more prevalent in the Andes, the Himalaya and the Alps ( [[#Tschakert--2019|Tschakert et al., 2019]] ). Often mentioned across studies is the loss of intrinsic memories and culture related to changes in world heritage landscapes and iconic sites ( [[#Jurt--2015|Jurt et al., 2015]] ; [[#Sherry--2018|Sherry et al., 2018]] ; [[#Bosson--2019|Bosson et al., 2019]] ). Changes in hazard landscapes are also reported to contribute to the loss of peace of mind and loss of well-being ( [[#Diemberger--2015|Diemberger et al., 2015]] ). Overall, there is ''limited evidence but medium agreement'' that the risk of intangible losses and the loss of cultural identity will rapidly increase and that consequences will go from reversible damage to irreversible losses ( [[#Tschakert--2019|Tschakert et al., 2019]] ).

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